More recently, an increasing number of works [1]}, [2]}, [3]} have focused on joint learning of graph structures and graph neural networks to model the dependencies between nodes.
LDS [2]} models the edges as random variables whose parameters are treated as hyperparameters in a bilevel learning framework.
The random variables parameterize the element-wise Bernoulli distribution from which the adjacency matrix \(\mathbf {A}\)  is sampled.
GTS [3]} introduces a neighborhood graph as a regularization that improves graph quality and reformulates the problem as a unilevel optimization problem.
Notably, We follow the framework of GTS but enhance it by the pre-training model since TSFormer gives better latent representations of time series for calculating their correlations.
